World Leaders
Military Serviece
and Propensity for War

MAJ Dusty Turner

Okay, enough of that

Grant
Grant

Kennedy
Kennedy

Nixon
Nixon

Bush
Bush

Grant
Grant

Kennedy
Kennedy

Nixon
Nixon

Bush
Bush

Disclaimer

The research presented in this document does not represent the official views of the Army. The opinions and findings expressed herein are solely those of the authors or contributors and do not reflect Army policy or endorsement.

Are leaders with military experience more likely to lead their countries in war?

But first

Why are you at Baylor and why are you trying to answer this question…?

Army

  • Engineer Officer
    • Training: Fort Leonard Wood Missouri
    • Platoon Leader: Hawaii (Iraq)
    • Company Commander: White Sands Missile Range, NM (Afghanistan)
  • Assistant Professor / Instructor
    • United States Military Academy, West Point, NY
  • Operations Research Systems Analyst (ORSA)
    • Center for Army Analysis: Fort Belvoir, VA

Baylor

  • Dr (COL R) Rodney Sturdivant

  • Applied Logistic Regression

  • Associate Professor and Director of the Statistical Consulting Center

  • Dancing with the Waco Stars

Dr Peter Campbell

  • Associate Professor of Political Science at Baylor University
  • PhD in Political Science from the University of Notre Dame
  • Author of “Military Realism: The Logic and Limits of Force and Innovation in the US Army
  • Expert in:
    • Civil-military relations
    • Insurgency and counterinsurgency
    • The just war tradition
    • Cyber warfare

Are leaders with military
experience more likely to
lead their countries in war?

Now, back to the point…

Those with listed experience

Now, back to the point…

Those without listed experience

What I assume about you…

Statistics

R

Real World Question

What I assume about you…

Statistics

\(\log\left(\frac{p}{1-p}\right) = \beta_0 + \beta_1X_1 + \beta_2X_2 + \ldots + \beta_kX_k\)

What I assume about you…

Statistics

R

Real World Question

What I assume about you…

R

glm(formula = y ~ x, family = binomial(link = "logit"))

What I assume about you…

Statistics

R

Real World Question

What I assume about you…

Real World Question

Grant Bush Nixon Kennedy

What I assume about you…

Real World Question

Grant Bush Nixon Kennedy

What I assume about you…

Statistics

R

Real World Question

The Leader Experience and Attribute Descriptions (LEAD) data set

  • Information assembled from
    • Previously assembled data sets
    • Academic books and articles
    • Obituaries
    • Genealogical databases
    • Military and state archives
    • U.S. Presidential Libraries
    • Photographs

Explore

Explore

Model Building Techniques

  • Forward Selection
  • Backwards Selection
  • All Subsets
  • Purposeful Selection

Purposeful Selection

  • Univariable Analysis

  • Initial Multivariable Model

  • Compare Models

  • Preliminary Main Effects Model

  • Main Effects Model

  • Preliminary Final Model

  • Adequacy and Model Fit

Purposeful Selection



  • Univariable Analysis
  • Adequacy and Model Fit

Univariable Analysis

univariable <- function(factor) {

  formula <- str_c("cwinit", "~", factor)
  
  glm(formula, data = data, family = binomial(link = "logit")) |> 
  broom::tidy() |> 
  select(term, estimate, p.value) |> 
  filter(term != "(Intercept)")

}

Univariable Analysis

univariable <- function(factor) {

  formula <- str_c("cwinit", "~", factor)
  
  glm(formula, data = data, family = binomial(link = "logit")) |> 
  broom::tidy() |> 
  select(term, estimate, p.value) |> 
  filter(term != "(Intercept)")

}

Univariable Analysis

univariable <- function(factor) {

  formula <- str_c("cwinit", "~", factor)
  
  glm(formula, data = data, family = binomial(link = "logit")) |> 
  broom::tidy() |> 
  select(term, estimate, p.value) |> 
  filter(term != "(Intercept)")

}

Univariable Analysis

univariable <- function(factor) {

  formula <- str_c("cwinit", "~", factor)
  
  glm(formula, data = data, family = binomial(link = "logit")) |> 
  broom::tidy() |> 
  select(term, estimate, p.value) |> 
  filter(term != "(Intercept)")

}

Univariable Analysis

univariable <- function(factor) {

  formula <- str_c("cwinit", "~", factor)
  
  glm(formula, data = data, family = binomial(link = "logit")) |> 
  broom::tidy() |> 
  select(term, estimate, p.value) |> 
  filter(term != "(Intercept)")

}


univariable(factor = "age") |>
  gt::gt() |> 
  gt::fmt_number()
term estimate p.value
age 0.01 0.00

Univariable Analysis

future_map_dfr(
  .x = factors_of_interest,
  .f = ~univariable(factor = .x),
  .progress = T
)

Univariable Analysis

term estimate p.value odds
monadicleaderrisk 6.52 0.00 675.98
year −0.01 <0.01 0.99
syscon 9.72 <0.01 16,591.53
leadernoinit −0.20 <0.01 0.82
defpact 1.13 <0.01 3.10
satisdy 1.63 <0.01 5.09
warwin 0.89 <0.01 2.44
rebelloss 1.21 <0.01 3.34
leadernoinit2 −0.01 <0.01 0.99
countryrandom 0.49 <0.01 1.64
leaderpeaceyrs1 <0.01 <0.01 1.00
combat 0.48 <0.01 1.62
leaderpeaceyrs2 <0.01 <0.01 1.00
cwpceyrs1 <0.01 <0.01 1.00
leaderpeaceyrs3 <0.01 <0.01 1.00
leadernoinit3 0.00 <0.01 1.00
sideabof 0.53 <0.01 1.70
cwpceyrs2 <0.01 <0.01 1.00
rebel 0.33 <0.01 1.39
cwpceyrs3 <0.01 <0.01 1.00
rebelwin 0.46 <0.01 1.58
careerpolitician −0.32 <0.01 0.73
religion 0.74 <0.01 2.10
age 0.01 <0.01 1.01
warloss 0.40 <0.01 1.49
marriedinpower −0.38 <0.01 0.69
business −0.44 <0.01 0.64
medicine −0.87 <0.01 0.42
militarycareer 0.24 <0.01 1.27
milnoncombat 0.30 <0.01 1.35
polity21 −0.01 <0.01 0.99
numGPs 0.08 <0.01 1.09
divorced −0.24 <0.01 0.79
teacher 0.19 <0.01 1.21
parstability 0.25 <0.01 1.28
totalspouses −0.06 <0.01 0.94
dem1 −0.15 <0.01 0.86
demlow −0.01 <0.01 0.99
married −0.31 <0.01 0.74
jointdem −0.25 <0.01 0.78
activist 0.14 <0.01 1.15
illegit −0.36 <0.01 0.70
childtotal −0.01 0.03 0.99
leveledu −0.05 0.03 0.95
gender 0.34 0.07 1.41
aristocratlandowner −0.14 0.07 0.87
dem2 0.06 0.17 1.07
law −0.07 0.19 0.93
police −0.26 0.26 0.77
orphanbinary −0.14 0.28 0.87
officetenure1000 −0.01 0.41 0.99
bluecollar −0.04 0.50 0.96
royalty −0.04 0.52 0.96
scienceeng 0.04 0.70 1.04
fatalmid 22.13 0.77 4,077,739,297.16
demhigh <0.01 0.78 1.00
force2dv 23.28 0.81 12,894,882,352.48
random <0.01 0.85 1.01
dyadid <0.01 0.96 1.00
journalism <0.01 0.99 1.00
creative 0.00 1.00 1.00

Final Model Option

term log_odds odds_ratio p.value
(Intercept) −3.71 0.02 <0.01
log_yearssincemidinit −1.16 0.31 <0.01
illegit1 −0.56 0.57 <0.01
medicine1 −0.45 0.64 0.07
royalty1 −0.36 0.69 <0.01
militarycareer1 −0.26 0.77 0.09
age 0.01 1.01 0.02
officetenure1000 0.03 1.03 0.03
parstability1 0.32 1.37 0.08
yearssincemidinit 0.35 1.42 <0.01
combat1 0.54 1.71 <0.01
creative1 0.56 1.74 0.04
rebelloss1 0.68 1.97 <0.01
milnoncombat1 0.70 2.01 <0.01

Final Model

Leader Risk Score

Leader Risk To_War
Ronald Reagan 0.74 Yes
Gerald Ford 0.59 Yes
John F. Kennedy 0.58 Yes
Theodore Roosevelt 0.56 Yes
Bush 0.54 Yes
Cleveland 0.21 No
Roosevelt, F. 0.21 Yes
Coolidge 0.20 No
Hoover 0.19 No
Cleveland 0.17 No
Leader Risk To_War
John F. Kennedy 0.58 Yes
Adolf Hitler 0.57 Yes
Nixon 0.52 Yes
G.W. Bush 0.39 No
Grant 0.36 Yes
Mandela 0.28 No

Outliers

LogisticDx::dx(final_model, byCov=T)

explain what some of these are

\(\eqn{\mathrm{dr} \quad dr_i}{dr}\)

Outliers

So lets talk…

  • Data Issues
  • Causality

to do

remove some univariable analysis factors

rework actual final model – maybe do not include combat or milnoncombat

create visual to show effect of combat

have closing slide